Systems and methods for predicting tissue viability deficits from physiological, anatomical, and patient characteristics

ABSTRACT

Systems and methods are disclosed for using patient-specific anatomical models and physiological parameters to predict viability of a target tissue or vessel to guide diagnosis or treatment of cardiovascular disease. One method includes: receiving a patient-specific vessel model and a patient-specific tissue model of a patient anatomy; receiving one or more patient-specific physiological parameters (e.g. blood flow, anatomical characteristics, etc.) for one or more physiological states; estimating a viability characteristic of the patient-specific tissue or vessel model (e.g., via a trained machine learning algorithm), using the patient-specific physiological parameters; and outputting the viability characteristic to an electronic storage medium or display.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Application No. 62/142,172, filed Apr. 2, 2015, the contentsof which are hereby incorporated herein by reference in their entirety.

FIELD OF DISCLOSURE

Various embodiments of the present disclosure relate generally todisease assessment, treatment planning, and related methods. Morespecifically, particular embodiments of the present disclosure relate tosystems and methods for assessing and treating ischemia.

BACKGROUND

Ischemia is a common ailment that affects millions of people. Ischemiais a restriction in the blood supply to biological tissues causing ashortage of oxygen and glucose needed for cellular metabolism. As aresult, damage or dysfunction may result in the tissues, and a localanemia may develop in parts of the body resulting from congestion. Apatient suffering from ischemia may experience irreversible damage tobodily tissues in as little as 20 minutes. A more severe manifestationof disease may lead to tissue necrosis and/or gangrene. Significantstrides have been made in the measurement of ischemia including usingspecialized imaging techniques (e.g., Contrast-Enhanced MagneticResonance Imaging (CEMRI), Fludeoxyglucose Positiron Emission Tomography(FDG-PET), stress echo/MRI, multidetector CT, and/or dual energy CT).However, these imaging techniques may incur a significant financialexpense and may also expose a patient to additional radiation.Furthermore, the equipment required to perform the specialized imagingtechniques may not be available at some facilities. Since viability isthe degree to which a vessel, tissue, or organ is functional, anischemia may result in reduced viability of the underlying vessel,tissue, or organ. Thus, a desire exists to use available patientinformation to estimate a viability characteristic in a target tissue,where the estimated viability data may be obtained by machine learningfrom a patient-specific vascular and/or anatomical model, and by usingany other additional data that may be available. Since the vascularand/or anatomical model may be derived from several imaging techniques,the embodiments of the present disclosure may enable use of a singlescan to assess both tissue anatomy and tissue viability.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for using available patient information toestimate viability of a target tissue or vessel to guide diagnosis ortreatment of cardiovascular disease.

One method includes: receiving a patient-specific vessel model or tissuemodel of a patient anatomy; receiving one or more patient-specificphysiological parameters for one or more physiological states;estimating a characteristic of tissue viability of the patient-specificvessel model or target tissue model, using the patient-specificphysiological parameters; and outputting the estimated characteristic oftissue viability to an electronic storage medium or display.

In accordance with another embodiment, a system for estimatingpatient-specific tissue viability, the system comprising: a data storagedevice storing instructions for determining characteristics of tissueviability; and a processor configured to execute the instructions toperform a method including the steps of: receiving a patient-specificvessel model or tissue model of a patient anatomy; receiving one or morepatient-specific physiological parameters for one or more physiologicalstates; estimating a characteristic of tissue viability of thepatient-specific vessel model or target tissue model, using thepatient-specific physiological parameters; and outputting the estimatedcharacteristic of tissue viability to an electronic storage medium ordisplay.

In accordance with another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for estimating acharacteristic of tissue viability, the method comprising: receiving apatient-specific vessel model or tissue model of a patient anatomy;receiving one or more patient-specific physiological parameters for oneor more physiological states; estimating a characteristic of tissueviability of the patient-specific vessel model or target tissue model,using the patient-specific physiological parameters; and outputting theestimated characteristic of tissue viability to an electronic storagemedium or display.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages on the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the detailed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments,and together with the description, serve to explain the principles ofthe disclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network forpredicting tissue viability to guide diagnosis or treatment of ischemia,according to an exemplary embodiment of the present disclosure.

FIG. 2 is a block diagram of a general method of estimating tissueviability, according to a general embodiment of the present disclosure.

FIG. 3 is a block diagram of an exemplary method of estimating tissueviability, according to an exemplary embodiment of the presentdisclosure.

FIG. 4 is a block diagram of an exemplary method of estimating tissueviability using machine learning, according to an exemplary embodimentof the present disclosure. FIG. 4 may also disclose a method ofperforming steps 208 or 322 in FIG. 2 and FIG. 3, respectively, which isdetermining an estimate of tissue viability.

FIG. 5 is a block diagram examining the method disclosed in FIG. 4 inmore detail. Furthermore, FIG. 5 discloses an exemplary method forestimating patient-specific viability characteristics from vesselgeometry and physiological information, using machine learning,according to an exemplary embodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

Ischemia is a common ailment, by which blood flow to the bodily tissuesmay be restricted. While significant strides have been made in thetreatment of ischemia, the treatment is often misplaced or excessive.For example, patients often undergo scans which may be costly and/orexpose the patient to unnecessary radiation. Patients are sometimessubjected to treatments that may not change their condition. In somesituations, patients even undergo treatments that ultimately worsentheir condition. Thus, a need exists to accurately assess the severityof ischemia and/or predicting ischemia to aid in selecting a course oftreatment. For the purposes of the disclosure: “patient” may refer toany individual or person for whom diagnosis or treatment of ischemia isperformed or characteristics of tissue viability are being estimated, orany individual or person associated with the diagnosis, treatment, ortissue viability analysis of one or more individuals.

Since viability is the degree to which a vessel, tissue, or organ isfunctional, an ischemia may result in reduced viability of theunderlying vessel, tissue, or organ. The embodiments of the presentdisclosure may provide systems and methods of using available patientinformation to estimate a viability characteristic in a target tissue,where the estimated viability data may be obtained by machine learningfrom a patient-specific vascular and/or anatomical model, and/or byusing any other additional data that may be available. A “target tissue”may refer to a tissue and/or organ in which the blood supply and/orviability characteristics may be estimated. Since the vascular and/oranatomical model may be derived from several imaging techniques, theembodiments of the present disclosure may enable use of a single scan toassess both tissue anatomy and tissue viability.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system 100 and network for estimating tissue viability toguide diagnosis or treatment of cardiovascular disease, according to anexemplary embodiment. Specifically, FIG. 1 depicts a plurality ofphysicians 102 and third party providers 104, any of whom may beconnected to an electronic network 101, such as the Internet, throughone or more computers, servers, and/or handheld mobile devices.Physicians 102 and/or third party providers 104 may create or otherwiseobtain images of one or more patients' anatomy. The physicians 102and/or third party providers 104 may also obtain any combination ofpatient-specific information, such as age, medical history, bloodpressure, blood viscosity, patient activity or exercise level, etc.Physicians 102 and/or third party providers 104 may transmit theanatomical images and/or patient-specific information to server systems106 over the electronic network 101. Server systems 106 may includestorage devices for storing images and data received from physicians 102and/or third party providers 104. Server systems 106 may also includeprocessing devices for processing images and data stored in the storagedevices.

FIG. 2 depicts a general embodiment of an exemplary method 200 forestimating tissue viability to guide diagnosis or treatment of ischemia.The method of FIG. 2 may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 101.

In one embodiment, step 202 may include receiving a patient-specificvessel model and a patient-specific target tissue model of tissuesupplied with blood from the vessels of the vessel model of a patientanatomy in an electronic storage medium of the storage system 106. An“electronic storage medium” may include, but is not limited to, a harddrive, network drive, cloud drive, mobile phone, tablet, or the likethat may or may not be affixed to a display screen. Specifically,receiving the patient-specific vessel model and/or patient-specifictarget tissue model may include either generating the patient-specificanatomical model at the server system 106, or receiving one over anelectronic network (e.g., electronic network 101). The patient-specificvessel model and patient-specific target tissue model may include acardiovascular model or any other anatomical model of a biologicaltissue or system of a specific person. In one embodiment, the vesselmodel and target tissue model may be derived from images of the personacquired via one or more available imaging or scanning modalities (e.g.,computed tomography (CT) scans and/or magnetic resonance (MR) imaging).For example, step 202 may include receiving CT and/or MR images of aperson's heart. Step 202 may further include generating, from thereceived images, a patient-specific vessel model and target tissue modelfor the particular person.

In one embodiment, step 204 and 206 may include receiving or calculatingone or more patient-specific physiological parameters. Thesepatient-specific physiological parameters may be received or calculatedfrom the received vessel model, the received target tissue model, one ormore medical images of the patient, and/or medical records of thepatient. These patient-specific physiological parameters may includeanatomical characteristics, as well as secondary characteristics relatedto the patient and/or the patient's anatomy (e.g., patientcharacteristics, disease burden characteristics, and electromechanicalmeasurements). The patient-specific physiological parameters may alsoinclude parameters related to blood circulation, including an estimationof the blood supply to each area of a target tissue and/or blood flowcharacteristics, under one or more physiological states.

One instance of a physiological state may be a resting state. Anotherphysiological state may be a physiological state other than the restingstate, or an “active” physiological state. Active physiological statesmay include hyperemia, various levels of exercise, post prandial,positional (e.g., supine-upright), gravitational (e.g. G-forces, zerogravity, etc.), or a combination thereof. In one embodiment, stepsand/or 206 may further include determining, specifying, and/or selectingone or more physiological states by comparing physiological parametersat a physiological state different from a patient resting state. In oneembodiment, the patient-specific physiological parameters may beobtained from sources other than the vessel model and/or target tissuemodel.

Specifically, step 204 may include receiving or calculating one or moreanatomical characteristics, patient characteristics, disease burdencharacteristics, and/or electromechanical characteristics, under one ormore physiological states. Step 204 may further include receiving orcalculating one or more image characteristics, e.g., derived fromlocations of the patient-specific vessel model or locations of thepatient-specific target tissue model. For example, image characteristicsmay be determined from renderings of regions, points, or sections of thepatient-specific vessel model or the patient-specific target tissuemodel.

In one embodiment, step 206 may include receiving or calculating anestimated supplied blood to each area of a target tissue or to eachvessel in a vascular network and/or estimated blood flowcharacteristics, under one or more physiological states. Theseestimations may be based on a measurement (e.g., by measuring throughimaging) or via an estimation of supplied blood in a resting state(e.g., based on a 3D simulation, a 1D simulation, or a learnedrelationship).

In one embodiment, step 208 may include determining an estimation ofviability in one or more vessels and/or areas of a target tissue, usingjoint prior information. The joint prior information may refer to theone or more physiological parameters determined from steps 204 and 206.In one embodiment, determining an estimation of viability may involvedetermining an estimation of supplied blood at one or more vessellocations of the person's vessel model, while the person is in one ormore physiological states, in order to determine viability of a tissueand/or vessel. The determination of tissue and/or vessel viability mayalso be based on a measurement of blood flow characteristics (e.g., byimaging) or via an estimation of blood flow characteristics in one ormore physiological states (e.g., based on a 3D simulation, a 1Dsimulation, or a learned relationship). In one embodiment, determiningtissue viability may include an estimation of the perfusion territoriesof the target tissue related to the vascular model. The estimation ofperfusion territories may be determined by using a nearest-neighbor(e.g., Voronoi diagram) approach to assign locations in the targettissue to the closest supplying vessel in the vascular model. Theestimation of perfusion territories may also be determined using amicrovascular estimation technique from an anatomical model, forexample, by using a constrained constructive optimization approach. Inone embodiment, step 208 may include estimating viability at one or morelocations of the target tissue and/or vessel in one or morephysiological states using a trained machine learning algorithm. Step208 may be performed by a processor.

In one embodiment, step 210 may include outputting the estimation ofviability of the tissue and/or vessel to an electronic storage medium(e.g., hard disk, network drive, portable disk, smart phone, tabletetc.) and/or to a display screen. In one embodiment, the outputviability estimates may be displayed in greyscale or color in 2D or 3D.The calculated tissue and/or vessel viability estimates may be overlaidon the anatomical model of the target tissue and/or overlaid on an imageof the target tissue. In one embodiment, step 210 may include estimatingtissue viability designed to simulated a SPECT or PET scan in one ormore physiological states. In one embodiment, the estimation may beperformed by modeling contrast agent in the concentrations given by theviability estimates. In another embodiment, the estimation may involveperforming a Monte Carlo simulation to estimate the collimation ofphotons or positrons at virtual collimator locations. Using thecollimator estimation, a SPECT or PET image may be reconstructed usingstandard tomographic techniques. The estimated tissue viabilitycharacteristics may be saved to an electronic storage medium and/ordisplayed on a monitor.

FIG. 3 depicts an exemplary embodiment of method 300 for estimatingtissue viability to guide diagnosis or treatment of ischemia. The methodof FIG. 3 may be performed by server systems 106, based on information,images, and data received from physicians 102 and/or third partyproviders 104 over electronic network 101.

In one embodiment, step 302 may include receiving a patient-specificvessel model or target tissue model of a patient anatomy in anelectronic storage medium of the storage system 106. Specifically,receiving the patient-specific vessel model or target tissue model mayinclude either generating the patient-specific vessel model or targettissue model at the server system 106, or receiving the patient-specificvessel model or target tissue model over an electronic network (e.g.,electronic network 101). In one embodiment, the vessel model or targettissue model may be derived from images of the person acquired via oneor more available imaging or scanning modalities (e.g., CT scans and/ormagnetic resonance imaging). For example, step 302 may include receivingCT and/or MRI images of a person's heart. Step 302 may further includegenerating, from the received images, a patient-specific cardiovascularmodel, a model of any biological system for the particular person, or atarget tissue of the particular person.

In one embodiment, step 304 may include receiving or calculating anestimation of one or more of the anatomical characteristics of thetarget tissue. The anatomical characteristics may include, but are notlimited to, vessel size, vessel shape, tortuosity, thickness, and thelike. This calculation may be based on a measurement (e.g., by measuringthe anatomical characteristics from imaging) or via an estimation of theanatomical characteristics in a resting state (e.g., based on a 3Dsimulation, a 1D simulation, or a learned relationship).

In one embodiment, step 306 may include receiving or calculating one ormore image characteristics of the target tissue of the vascular modelfrom one or more medical images of the patient. The medical images maybe in the form of CT scan images, MRI images, ultrasound images, PETimages, or SPECT images. The images may capture the vascular model inone or more physiological states (e.g., rest, stress, active). The imagecharacteristics of the target tissue or vessels may be received orcalculated for one or more locations of the vascular system or targettissue. The image characteristics may include, but are not limited to,local average intensities at one or more image resolutions, differencesof the average intensities (e.g., calculated via wavelet bases, usingfor example, Haar wavelets), texture characteristics (e.g., Haralicktexture features), any standard image features including histograms ofgradients, SIFT, steerable filters, and characteristics related to animaging or scanning modality, etc.

In one embodiment, step 308 may include receiving patientcharacteristics. The patient characteristics may include, but are notlimited to, age, gender, smoking history, height, weight, diabeticstatus, hypertensive status, ethnicity, family history, blood type,prior history of drug use, and/or genetic history. The patientcharacteristics may be obtained via the electronic network 100 or fromthe patient's physician 102 or from a third party provider 103.

In one embodiment, step 310 may include receiving or calculating diseaseburden characteristics of the target tissue and/or vessels. The diseaseburden characteristics may include, but are not limited to, the presenceand extent of plaque buildup within the arteries, the presence of plaquecharacteristics (e.g., spotty calcification, low attenuation plaque,napkin-ring sign, positive remodeling), patient level or vessel levelcalcium scores, tissue viability information, vessel wall motion, vesselwall thickness, and/or ejection fraction.

In one embodiment, step 312 may include, receiving or calculatingelectromechanical measurements. The electromechanical measurements mayinclude, but are not limited to, electrocardiography (ECG) measurements,or invasive electrophysiology (EP) measurements.

In one embodiment, step 314 may include receiving or calculating anestimation of the supplied blood to each area of the target tissue underone or more physiological states. One instance of a physiological statemay be a resting state. This calculation may be based on a measurement(e.g., by measuring by imaging) or via an estimation of supplied bloodin a resting state (e.g., based on a three-dimensional (3D) simulation,a one-dimensional (1D) simulation, or a learned relationship). Anotherphysiological state may be a physiological state other than the restingstate, or an “active” physiological state. One instance of such aphysiological state may include hyperemia. Other non-restingphysiological states may include, various levels of exercise, postprandial, positional (e.g., supine-upright), gravitational (e.g.G-forces, zero gravity, etc.).

In one embodiment, step 316 may include receiving or calculating one ormore blood flow characteristics of the target tissue. In one embodiment,the blood flow characteristic may include, but is not limited to, afractional flow reserve value (FFR), flow direction and/or flowmagnitude, and may be determined by an estimation of blood flow to thetarget tissue. In one embodiment, the blood flow characteristic may becalculated by several means, including, but not limited to, invasivemeasurements (e.g., invasive FFR, thrombosis in myocardial infarction(TIMI), or microspheres), calculation using a blood flow simulationmodel (e.g., a 3D or 1D fluid simulation model, calculation, or TAFE),calculation using image characteristics (e.g., TAG or CCO) derived fromone or more medical images, or calculation using a machine learningestimation of blood supply based on anatomical or imaging features. Inone embodiment, step 316 may include calculating an estimation of theblood flow in the perfusion territories of the target tissue related tothe vessel model. This estimation may be determined by using anearest-neighbor (e.g., Voronoi diagram) approach to assigning locationsin the target tissue to the closest supplying blood vessel in thevascular model. The estimation may also be determined using amicrovascular estimation technique from an anatomical model, forexample, by using a constrained constructive optimization approach. Inone embodiment, step 316 may be performed by a processor. The processormay estimate tissue viability at one or more locations of the targettissue in the vascular model in one or more psychological states bymachine learning.

In one embodiment, step 318 may include determining an estimate of oneor more tissue viability characteristics at one or more locations in thetarget tissue related to the vascular model. The estimation of tissueviability characteristics may be calculated for one or morephysiological states using the one or more physiological parameters(e.g., anatomical characteristics, estimated blood supply, estimatedblood flow characteristics, estimated perfusion territories, patientcharacteristics, disease burden characteristics, and/orelectromechanical measurements) and/or image characteristics from one ormore medical images. In one embodiment, this calculation of tissueviability may be performed by training a machine learning algorithmusing a database of patients with known tissue viability characteristicsand known patient-specific physiological parameters, including, but notlimited to, the anatomical characteristics, estimated perfusionterritories, disease burden characteristics, and/or electromechanicalmeasurements. In one embodiment step 318 may be performed using aprocessor.

In one embodiment, step 320 may include outputting the estimation oftissue viability to an electronic storage medium (e.g., hard disk,network drive, portable disk, smart phone, tablet, etc.) and/or to adisplay screen. In one embodiment, the output tissue viability estimatesmay be displayed in greyscale or color in 2D or 3D. In one embodiment,the output tissue viability estimates may be overlaid or superimposed onthe anatomical model of the target tissue and/or overlaid orsuperimposed on an image of the target tissue. In one embodiment, thisdetermination may be performed by training a machine learning algorithmusing a database of patients with known tissue viability characteristicsand known patient-specific physiological parameters.

The above recited steps of methods 200 and 300 may be used to estimatetissue viability in a variety of biological tissues, including, but notlimited to, the myocardium using a coronary vascular model, the brainusing a cerebral vascular model, muscle tissue using a peripheralvascular model, the liver using a hepatic vascular model, the kidneyusing a renal vascular model, the bowel using a visceral vascular model,and in other organs including the spleen and pancreas, using a vascularmodel for vessels supplying blood to the target organ.

In one embodiment, the tissue viability estimation may also be used toenhance a blood flow simulation by using more accurate boundaryconditions to perform a simulation or estimation of blood flowcharacteristics.

In one embodiment, treatment planning and diagnosis may be improved byvirtually changing the input information (e.g. the vascular model,tissue model, patient-specific physiological parameters, etc.). Suchchanges may include virtual revascularization of the vascular model,modifying the tissue model, patient characteristics, etc.). Yet anotherembodiment may include predicting the effects on tissue viability in thetarget tissue based on the changed inputs (e.g., predicting thefunctional recovery of the target tissue in response to a changed inputusing the estimated viability information).

FIG. 4 depicts an exemplary embodiment of method 400 for training amachine learning algorithm to determine an estimate of tissue viabilityat one or more locations in the target tissue in one or morephysiological states. The method of FIG. 4 may be performed by serversystems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network101.

In one embodiment, step 402 may include assembling a database containingone or more known physiological parameters from a plurality of patientsat one or more locations of a vascular and/or target tissue model andthe estimated or measured tissue viability at those locations. For thepurpose of disclosure, “physiological parameters” may refer to one ormore of the received or calculated anatomical characteristics, perfusionterritories, blood supply to the target tissue, blood flowcharacteristics, patient characteristics, disease burdencharacteristics, and/or electromechanical measurements. The models maybe obtained from one or more available imaging or scanning modalities,including, but not limited to, PET, SPECT, MR, CT, and/or a combinationof these modalities. In one embodiment, the database may also includeimage characteristics derived from one or more medical images.

In one embodiment, step 404 may include training a machine learningalgorithm to map the one or more physiological parameters at one or morelocations of the vascular and/or target tissue model to the one or morecharacteristics of tissue viability those locations. The machinelearning algorithm may take many forms, including, but not limited to, amulti-layer perceptron, deep learning, support vector machines, randomforests, k-nearest neighbors, Bayes networks, etc. In one embodiment,image characteristics derived from one or more medical images may alsobe mapped along with the one or more physiological parameters, at one ormore locations of the vascular and/or target tissue model, to the one ormore characteristics of tissue viability those locations.

In one embodiment, step 406 may include applying the trained machinelearning algorithm to the set of patient-specific physiologicalparameters of the vascular model and/or target tissue obtained from anew patient to estimate the tissue viability at one or more locations.In another embodiment, step 406 may include applying the trained machinelearning algorithm to a set that includes patient-specific physiologicalparameters of the vascular model and/or target tissue obtained from anew patient and image characteristics derived from one or more medicalimages of the new patient, to estimate the tissue viability at one ormore locations.

FIG. 5 is a block diagram of an exemplary method for estimatingpatient-specific tissue viability from vessel geometry and physiologicalinformation, according to an exemplary embodiment of the presentdisclosure. The method of FIG. 5 may be performed by server systems 106,based on information received from physicians 102 and/or third partyproviders 104 over electronic network 100.

In one embodiment, the method of FIG. 5 may include a training method502, for training one or more machine learning algorithms based onnumerous patients' physiological parameters and tissue viabilityestimates, and a production method 504 for using the machine learningalgorithm results to predict a particular patient's tissue viabilitycharacteristics.

In one embodiment, training method 502 may involve acquiring, for eachof a plurality of individuals, e.g., in digital format: (a) apatient-specific geometric model, (b) one or more measured or estimatedphysiological parameters, and (c) tissue viability characteristics.Training method 502 may then involve, for one or more points in eachpatient's model, creating a feature vector of the patients'physiological parameters and associating the feature vector with tissueviability characteristics. Image characteristics from one or moremedical images, for each of a plurality of individuals, may also beincluded with the one or more measured or estimated physiologicalparameters for the purpose of creating the feature vector. Trainingmethod 502 may then train a machine learning algorithm (e.g., usingprocessing devices of server systems 106) to predict tissue viability ateach point of a geometric model, based on the feature vectors andestimated tissue viability. Training method 502 may then save theresults of the machine learning algorithm, including feature weights, ina storage device of server systems 106. The stored feature weights maydefine the extent to which patient-specific physiological parameters oranatomical geometry are predictive of tissue viability characteristics.In another embodiment, training method 502 may be performed based on FFRestimates generated using computational fluid dynamics (CFD) techniquesfor a plurality of patients. Training method 502 may then involveassociating an estimated FFR with every point in a patient's geometricmodel, and then creating a feature vector of the patients' physiologicalparameters and associating the feature vector with FFR estimates. Imagecharacteristics from one or more medical images, for each of a pluralityof individuals, may also be included with the one or more measured orestimated physiological parameters for the purpose of creating thefeature vector. Training method 502 may then train a machine learningalgorithm (e.g., using processing devices of server systems 106) topredict tissue viability at each point of a geometric model, based onthe feature vectors and estimated FFR.

The production method 504 may involve estimating tissue viabilitycharacteristics for a particular patient, based on results of executingtraining method 502. In one embodiment, production method 504 mayinclude acquiring, e.g. in digital format: (a) a patient-specificgeometric model, and (b) one or more measured or estimated physiologicalparameters. For multiple points in the patient's geometric model,production method 504 may involve creating a feature vector of thephysiological parameters used in the training mode. In one embodiment,image characteristics derived from one or more medical images of theparticular patient may also be included with the one or more measured orestimated physiological parameters for the purpose of creating thefeature vector. Production method 504 may then use saved results of themachine learning algorithm to produce estimates of the patient's bloodflow and/or tissue viability characteristics for each point in thepatient-specific geometric model. Finally, production method 504 mayinclude saving the results of the machine learning algorithm, includingpredicted blood flow and/or tissue viability characteristics, to astorage device of server systems 106.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-20. (canceled)
 21. A computer-implemented method for estimating tissueviability of a patient's tissue, the method comprising: receiving, viaone or more processors, image data derived from one or more images of apatient's anatomy when a patient is at one or more physiological states;determining, via the one or more processors, one or more imagecharacteristics based on the image data; generating, via the one or moreprocessors, one or more patient-specific models based on the image data,each being a model of a vessel or tissue of the patient when the patientis at the one or more physiological states; calculating, via the one ormore processors, patient-specific values of the one or morephysiological or anatomical parameters based on the one or morepatient-specific models; computing, via the one or more processors, oneor more tissue viability characteristics based on the one or more imagecharacteristics and the patient-specific values of the one or morephysiological or anatomical parameters; and outputting, to an electronicstorage medium or a display, the computed tissue viabilitycharacteristics.
 22. The computer implemented method of claim 21,wherein the one or more physiological or anatomical parameters includeperfusion territories, the patient-specific values include a patienttissue perfusion territory estimation, the method comprises: estimatinga blood supply to one or more vessel or tissue areas, using a blood flowsimulation in at least one of the one or more patient-specific models;determining the patient tissue perfusion territory estimation based onthe estimated blood supply; modifying the patient-specific model or thepatient-specific values of the one or more physiological or anatomicalparameters; determining an effect of the modifying the patient-specificmodel or at least one of the patient-specific values of the one or morephysiological or anatomical parameters on the computed tissue viabilityvalue; generating a treatment plan based on the determined effect; andoutputting the treatment plan to the electronic storage medium or thedisplay.
 23. The computer implemented method of claim 21, wherein theone or more physiological or anatomical parameters include one or moreanatomical characteristics including vessel size, vessel shape, vesseltortuosity, vessel length, vessel thickness, or a combination thereof.24. The computer implemented method of claim 21, the one or morephysiological states include a resting patient state, a hyperemic state,an exercise state, a postprandial state, a gravitational state, anemotional state, a state of hypertension, a medicated state, or acombination thereof.
 25. The computer implemented method of claim 21,wherein the one or more tissue viability characteristics include ameasure of an extent to which a vessel, tissue, or organ is functional.26. The computer implemented method of claim 22, wherein the estimatedblood supply includes fractional flow reserve, flow magnitude, flowdirection, or a combination thereof.
 27. The computer implemented methodof claim 21, wherein the one or more image characteristics include, oneor more of: local average intensities, texture characteristics, andstandard image features.
 28. The computer implemented method of claim21, further including receiving one or more secondary characteristicsincluding patient characteristics, target tissue diseasecharacteristics, electromechanical measurements, or a combinationthereof.
 29. The computer implemented method of claim 21, furtherincluding comparing blood flow characteristics in the tissue or a vesselat different physiological states.
 30. The computer implemented methodof claim 21, wherein the one or more patient-specific models include: acoronary vascular model and a model of the myocardium; a cerebralvascular model and a model of the brain; a peripheral vascular model anda model of muscle; a hepatic vascular model and a model of a liver; arenal vascular model and a model of a kidney; a visceral vascular modeland a model of a bowel; or a vascular model representing a vessel and atarget organ to which blood is supplied by the vessel.
 31. The computerimplemented method of claim 21, further comprising: adjusting thepatient-specific values of the one or more physiological or anatomicalparameters based on the computed tissue viability characteristics; andsimulating a blood flow characteristic using the computed tissueviability characteristics and the adjusted patient-specific values. 32.The computer implemented method of claim 22, wherein the treatment planis generated based further on the modified patient-specific model or thepatient-specific values of the one or more physiological or anatomicalparameters.
 33. A system for estimating patient-specific tissueviability, the system comprising: a data storage device storinginstructions for determining characteristics of tissue viability; and aprocessor configured to execute the instructions to perform a methodincluding the steps of: receiving image data derived from one or moreimages of a patient's anatomy when a patient is at one or morephysiological states; determining one or more image characteristicsbased on the image data; generating one or more patient-specific modelsbased on the image data, each being a model of a vessel or tissue of thepatient when the patient is at the one or more physiological states;calculating patient-specific values of the one or more physiological oranatomical parameters based on the one or more patient-specific models;computing one or more tissue viability characteristics based on the oneor more image characteristics and the patient-specific values of the oneor more physiological or anatomical parameters; and outputting, to anelectronic storage medium or a display, the computed tissue viabilitycharacteristics.
 34. The system of claim 33, wherein the one or morephysiological or anatomical parameters include perfusion territories,the patient-specific values include a patient tissue perfusion territoryestimation, the method further comprises: estimating a blood supply toone or more vessel or tissue areas, using a blood flow simulation in atleast one of the one or more patient-specific models; determining thepatient tissue perfusion territory estimation based on the estimatedblood supply; modifying the patient-specific model or thepatient-specific values of the one or more physiological or anatomicalparameters; determining an effect of the modifying the patient-specificmodel or at least one of the patient-specific values of the one or morephysiological or anatomical parameters on the computed tissue viabilityvalue; generating a treatment plan based on the determined effect; andoutputting the treatment plan to the electronic storage medium or thedisplay.
 35. The system of claim 34, wherein the one or morephysiological states include a resting patient state, a hyperemic state,an exercise state, a postprandial state, a gravitational state, anemotional state, a state of hypertension, a medicated state, or acombination thereof.
 36. The system of claim 33, wherein the one or moretissue viability characteristics include a measure of the extent towhich a vessel, tissue, or organ is functional.
 37. The system of claim34, wherein the estimated blood supply includes fractional flow reserve,flow magnitude, flow direction, or a combination thereof.
 38. The systemof claim 33, wherein the estimating the tissue viability characteristicsincludes comparing blood flow characteristics in a tissue or a vessel atdifferent physiological states.
 39. The system of claim 33, wherein theone or more patient-specific models include: a coronary vascular modeland the myocardium; a cerebral vascular model and the brain; aperipheral vascular model and muscle; a hepatic vascular model and aliver; a renal vascular model and a kidney; a visceral vascular modeland a bowel; or a vascular model representing a vessel and a targetorgan to which blood is supplied by the vessel.
 40. A non-transitorycomputer readable medium for performing a method on a computer systemcontaining computer-executable programming instructions for estimating acharacteristic of tissue viability, the method comprising: receiving,via one or more processors, image data derived from one or more imagesof a patient's anatomy when a patient is at one or more physiologicalstates; determining, via the one or more processors, one or more imagecharacteristics based on the image data; generating, via the one or moreprocessors, one or more patient-specific models based on the image data,each being a model of a vessel or tissue of the patient when the patientis at the one or more physiological states; calculating, via the one ormore processors, patient-specific values of the one or morephysiological or anatomical parameters based on the one or morepatient-specific models; computing, via the one or more processors, oneor more tissue viability characteristics based on the imagecharacteristics and the patient-specific values of the one or morephysiological or anatomical parameters; and outputting, to an electronicstorage medium or a display, the computed tissue viabilitycharacteristics.